CN110147963B - Method and system for making small and medium schedule operation plan balance in assembled component - Google Patents

Method and system for making small and medium schedule operation plan balance in assembled component Download PDF

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CN110147963B
CN110147963B CN201910439529.6A CN201910439529A CN110147963B CN 110147963 B CN110147963 B CN 110147963B CN 201910439529 A CN201910439529 A CN 201910439529A CN 110147963 B CN110147963 B CN 110147963B
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汪浩
裴大茗
郝威巍
杨诚
吴美熹
李占
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China Institute Of Marine Technology & Economy
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Abstract

The invention discloses a method and a system for making a small-schedule operation plan balance in an assembled component. The method comprises the following steps: acquiring uncertainty factors in small and medium schedule operation plans of the assembly component; establishing an uncertainty algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors; determining a solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model; and determining the small-schedule operation plan in the assembly component according to the solving strategy. The method considers the uncertainty of various resource conditions such as time, space, personnel, equipment and the like in the construction process of the workshop assembly component, and has the advantages of wide application range, easy programming realization and high calculation efficiency.

Description

Method and system for making small and medium schedule operation plan balance in assembled component
Technical Field
The invention relates to the field of ship manufacturing, in particular to a small-schedule operation plan balance making method and a small-schedule operation plan balance making system in an assembly component.
Background
In modern shipbuilding models, the final product of a ship manufacturing enterprise is assembled from a large number of assembled components. In the production of domestic shipyards, the assembly components are difficult to move and transport in the construction process, and a fixed station construction mode is generally adopted. In the actual production process, the assembly components are positioned in regions, a lane production line is established, the building is completed on a jig frame at a fixed position in a production field, once the assembly components are fixed, the assembly components do not move until the building is completed, and in the process, materials, tools, equipment and personnel involved in the building of the assembly components are arranged around the building task of the assembly components, namely the assembly component fixed station building mode.
At present, the production of the assembly component product still adopts the manual production mode of the traditional discrete post responsibility system, an operator organizes the production according to the stage plan formulated according to experience, the scheduling is disordered and random, the production period is long, and the production efficiency is low. The building of the assembly component has the typical operation characteristics of product fixation and resource flow, in order to form the ordered, coordinated, controllable and efficient building execution effect of the assembly component, the advantages of combining the fixed assembly component building and the assembly line operation mode need to be considered, the process similarity principle is utilized, a small schedule operation plan balance making method in the assembly component under the uncertainty factor is established, and the professional team assembly line production is adopted on the fixed tire frame to realize the stream positioning professional production.
Disclosure of Invention
The invention aims to provide a method and a system for making a small-schedule operation plan balance in an assembly component, which consider the uncertainty of various resource conditions such as time, space, personnel, equipment and the like in the construction process of a workshop assembly component, and have the advantages of wide application range, easy programming realization and high calculation efficiency.
In order to achieve the purpose, the invention provides the following scheme:
a small and medium schedule operation plan balance making method in an assembly component comprises the following steps:
acquiring uncertainty factors in small and medium schedule operation plans of the assembly component;
establishing an uncertainty algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors;
determining a solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model;
and determining the small-schedule operation plan in the assembly component according to the solving strategy.
Optionally, the uncertainty factors include processing time and delivery time uncertainties.
Optionally, the establishing an uncertainty algorithm model of the small-schedule operation plan in the assembly component according to the uncertainty factor specifically includes:
and establishing an objective function mathematical model, a site scheduling algorithm model and a personnel scheduling algorithm model of the small schedule operation plan in the assembled component according to the uncertainty factors.
Optionally, the establishing a mathematical model of an objective function of a small-schedule operation plan in the assembled component according to the uncertainty factor specifically includes:
building continuous model of assembly component for building small schedule operation plan in assembly component according to uncertainty factor
Figure BDA0002071620240000021
And personnel utilization rate model
Figure BDA0002071620240000022
Wherein: f is a working daily system processing function and is set according to the actual production situation of the ship; si，j+1The starting time of the j +1 st procedure of the assembly component i; fi，jThe end time of the j process of the assembly component i; fi，maxThe final completion time for the assembled component i; si minThe start-up time of the assembly component i;
wherein: m is the total number of workers; c. CijThe actual labor time of the ith personnel on the jth day is in hours; h is the working time length of the working day; w is the number of working days of a month.
Optionally, the determining a solution strategy of the uncertainty algorithm model according to the uncertainty algorithm model specifically includes:
building a continuity model and the site scheduling algorithm model according to the assembly component, and determining a solving strategy of the site scheduling algorithm model;
and determining a solving strategy of the personnel scheduling algorithm model according to the personnel utilization rate model and the personnel scheduling algorithm model.
A small and medium schedule work plan balance making system in an assembly component comprises:
the acquisition module is used for acquiring uncertainty factors in small and medium schedule operation plans of the assembly component;
the uncertainty algorithm model determining module is used for establishing an uncertainty algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors;
the solving strategy determining module is used for determining the solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model;
and the medium and small schedule operation plan determining module is used for determining the medium and small schedule operation plan of the assembly component according to the solving strategy.
Optionally, the uncertainty factors include processing time and delivery time uncertainties.
Optionally, the uncertainty algorithm model determining module specifically includes:
the target function mathematical model determining unit is used for establishing a target function mathematical model of the small schedule operation plan in the assembly component according to the uncertainty factor;
the site scheduling algorithm model determining unit is used for establishing a site scheduling algorithm model of a small schedule operation plan in the assembled component according to the uncertainty factors;
and the personnel scheduling algorithm model determining unit is used for establishing a personnel scheduling algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors.
Optionally, the objective function mathematical model determining unit specifically includes:
the assembly component construction continuity model determining subunit is used for establishing an assembly component construction continuity model of a small schedule operation plan in the assembly component according to the uncertainty factors
Figure BDA0002071620240000031
Wherein: f is a working daily system processing function and is set according to the actual production situation of the ship; si，j+1The starting time of the j +1 st procedure of the assembly component i; fi，jThe end time of the j process of the assembly component i; fi，maxThe final completion time for the assembled component i; si minThe start-up time of the assembly component i;
a personnel utilization rate model determining subunit, configured to establish a personnel utilization rate model of the small-schedule operation plan in the assembled component according to the uncertainty factor
Figure BDA0002071620240000032
Wherein: m is the total number of workers; c. CijThe actual labor time of the ith personnel on the jth day is in hours; h is the working time length of the working day; w is the number of working days of a month.
Optionally, the solution policy determining module specifically includes:
the solving strategy determining unit of the site scheduling algorithm model is used for building a continuity model and the site scheduling algorithm model according to the assembly component and determining the solving strategy of the site scheduling algorithm model;
and the solving strategy determining unit of the personnel scheduling algorithm model is used for determining the solving strategy of the personnel scheduling algorithm model according to the personnel utilization rate model and the personnel scheduling algorithm model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a small and medium schedule operation plan balance making method for an assembly component, which comprises the following steps: acquiring uncertainty factors in small and medium schedule operation plans of the assembly component; establishing an uncertainty algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors; determining a solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model; determining a small-schedule operation plan in the assembled component according to the solving strategy; the method of the invention considers the uncertainty of various resource conditions such as time, space, personnel, equipment and the like in the construction process of the workshop assembly component, has the characteristics of wide application range, easy programming realization and the like, and obviously reduces the calculation amount and the analysis difficulty. The invention has wide application range, simple implementation process, convenient use in the production and construction stage of a shipyard and high efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for balancing and making a small-schedule work plan in an assembly member according to the present invention;
FIG. 2 is an example of a typical layout of an assembly component yard;
FIG. 3 is a diagram of a system for balancing and making a small schedule work plan in the assembled components of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for making a small-schedule operation plan balance in an assembly component, which consider the uncertainty of various resource conditions such as time, space, personnel, equipment and the like in the construction process of a workshop assembly component, and have the advantages of wide application range, easy programming realization and high calculation efficiency.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a flow chart of a method for balancing and making a small-schedule work plan in an assembly member according to the present invention. As shown in fig. 1, a method for balancing and making a small-schedule work plan in an assembly member includes:
step 101: acquiring uncertainty factors in a small schedule operation plan in an assembly component, wherein the uncertainty factors comprise uncertainty of machining time and delivery time;
the uncertainty factor in the small schedule operation plan in the assembling component is a fuzzy factor in the assembling component construction process. The assembly members inevitably generate a plurality of uncertain conditions when performing tasks in the actual construction process, and further influence the progress of each process. The configuration of each construction resource cannot be considered independently, mutual constraint and mutual influence exist among the resources, and uncertainty further reflects the whole project construction period through the time parameters of the process and the mutual influence. The uncertainty factors are of various types and relate to various aspects of the operation, personnel, tools, materials, fields and the like of the building process of the assembled components. The uncertainty of the production capacity comprises the absence of staff of a team, equipment failure, faults and the like; the order uncertainty comprises aspects such as production quality reworking, order temporary cancellation, emergency temporary order, order priority change and the like; uncertainty in production data includes differences in planned versus actual time, planned versus actual output, and so on.
And when the uncertainty factor classification in the small schedule operation plan in the assembled component is determined, quantitative mathematical description is carried out on the uncertainty of the processing time and the delivery time. The processing time is divided into 5 grades according to experience, and the actual processing time in the production process is not a definite discrete distribution variable but fuzzy change.
FIG. 2 is an example of a typical layout of an assembly component yard. Aiming at the problem of planning the operation of the assembly member, the machining time of the nth process of the task assembly member m by the k construction team can be represented by a rectangular fuzzy number T* j(Tj，ΔTj)，Tj-ΔTjRepresents the lower limit of the fuzzy interval, Tj+ΔTjRepresents the upper limit of the machining fuzzy interval; task j delivery date can also be treated with a rectangular ambiguity number d* j(dj，Δdj)，dj-ΔdjIndicating a lower delivery date limit, dj+ΔdjIndicating the upper limit of the delivery date. Aiming at the scheduling problem of the assembly component, the m nth procedure of the assembly component is processed by k construction teams for a rectangular fuzzy number T* mnk(Tmnk，ΔTmnk) Degree of membership function mumnk(x)：
Figure BDA0002071620240000051
X in the function represents a possible value of the fuzzy processing time.
Delivery date d for assembled member m* mnk(dmnk，Δdmnk) Function xi of degree of membershipmnk(x):
Figure BDA0002071620240000061
X in the function represents a possible value of the fuzzy lead time.
Step 102: establishing an uncertainty algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors; the method specifically comprises the following steps:
and establishing an objective function mathematical model, a site scheduling algorithm model and a personnel scheduling algorithm model of the small schedule operation plan in the assembled component according to the uncertainty factors.
The establishing of the objective function mathematical model of the small-schedule operation plan in the assembly component according to the uncertainty factors specifically comprises the following steps:
building continuous model of assembly component for building small schedule operation plan in assembly component according to uncertainty factor
Figure BDA0002071620240000062
And personnel utilization rate model
Figure BDA0002071620240000063
Wherein: f is a working daily system processing function and is set according to the actual production situation of the ship; si，j+1The starting time of the j +1 st procedure of the assembly component i; fi，jThe end time of the j process of the assembly component i; fi，maxThe final completion time for the assembled component i; si minThe start-up time of the assembly component i;
wherein: m is the total number of workers; c. CijThe actual labor time of the ith personnel on the jth day is in hours; h is the working time length of the working day; w is the number of working days of a month.
In the conventional production planning, the unified production and processing capacity is assumed to be fixed, and the time for processing a certain assembly component by a team is also fixed, but in the actual production process of the assembly component, the processing capacity is dynamically changed, and certain uncertainty exists in the processing time of a certain procedure of the assembly component. When describing the problem of uncertain scheduling of the assembly components, firstly, the uncertainty of the processing time and the delivery date needs to be described. We divide the machining time into N levels (usually N ═ 5) according to the actual production construction experience, and the machining time of the actual production construction process does not determine a discrete distribution variable but varies within a certain interval. Aiming at the problem of planning the operation of the assembled component, the machining time of the nth process of the assembled component m, which is machined by the k group, can adopt the rectangular fuzzy number T* mnk(Tmnk，ΔTmnk) Membership function mumnk(x) Indicates the machining time T* mnkWhen x is a measure of the likelihood of<Tmnk-ΔTmnkOr x>Tmnk-ΔTmnk，μmnkWhen T is equal to 0mnk-ΔTmnk<x<Tmnk+ΔTmnk，μmnk＝1，μmnk(x) X in (1) represents a possible value of the processing time. Delivery date d of assembled member m* mnk(dmnk，Δdmnk) The membership function ofAvailable rectangular fuzzy equation ximnk(x) The description is that: x is the number of<dmnk-ΔdmnkOr x>dmnk-Δdmnk，ξmnkWhen d is equal to 0mnk-Δdmnk<x<dmnk+Δdmnk，ξmnk＝1。dmnk-ΔdmnkFor maximum lead time of delivery, dmnk+ΔdmnkFor maximum late towing of delivery, ximnk(x) X in (1) represents a possible value of the delivery date. T ismnkFor a defined working time, Δ TmnkThe change value of the processing time is obtained; t is* mnkIs TmnkAnd Δ TmnkA function of (a); t is* mnkThere is no particular restriction on the writing method of (1).
The mathematical model of the uncertain scheduling problem of the assembly component comprises an objective function mathematical model, a site scheduling algorithm model and a personnel scheduling algorithm model. The objective function mathematical model comprises an assembly component building continuity objective, a personnel utilization objective and a manufacturing resource allocation objective. Site scheduling algorithm model essentially the above assumes the case of m parallel machines (site) and project work (building components). Working j has a working time T* mnkSubmission date is d* mnk. Work j must be composed of a member belonging to MjAnd (4) machining the assembly. The processing capacity (total length of the assembly components) of each machine (independent field of each small block) is limited, the assembly components of each independent small block are placed and modeled as a knapsack problem, and the assembly components needing to be arranged are selected by a dynamic programming algorithm; and the arrangement position of the selected assembly member is obtained by calculating a clustering rule. The personnel scheduling algorithm model then assumes that the order of production and location of the assembled components in the field has been determined, where the assignment of production personnel is accomplished. Since each assembled member has the largest producible number of persons, i.e. the number of working persons per process in the assembled member has an upper limit, and the number of persons working in one process of the assembled member cannot exceed this number. In the initial personnel allocation process, because the production personnel of the assembly members are sufficient, the upper limit number of the workers is allocated to each process of the assembly members until all the workersAnd (5) finishing the distribution. The distribution mode can ensure that processing personnel can be fully utilized in the initial production state, thereby providing a foundation for subsequent continuous production.
Constructing a continuity objective function model for the assembly member:
the continuity of the first part of the assembly member construction is mainly measured by the ratio of the idle time of the assembly member, and the ratio of the idle time of the assembly member construction is calculated as follows:
Figure BDA0002071620240000071
wherein: f is a working daily system processing function and is set according to the actual production situation of the ship; the amount of Si is controlled by the control system,j+1the starting time of the j +1 st procedure of the assembly component i; fi，jThe end time of the j process of the assembly component i; fi，maxThe final completion time for the assembled component i; si minThe start-up time of the assembly component i;
the above equation must satisfy the relationship:
Figure BDA0002071620240000081
Figure BDA0002071620240000082
smaller calculated r values indicate less idle time of the assemblage member and better assemblage member construction continuity.
An objective function model for personnel utilization. Personnel utilization rate reflects actual output efficiency of personnel in ship enterprises, and load balance and production organization improvement potential of workers can be analyzed.
The single-month utilization rate calculation formula of the labor personnel of the same work type is as follows:
Figure BDA0002071620240000083
wherein: m is the total number of workers; c. CijThe actual labor time of the ith personnel on the jth day is in hours; h is the working time length of the working day; w is the number of working days of a month.
The higher the obtained calculation result u value is, the higher the utilization rate of the workers of the work type is. Considering the utilization rate of all labor personnel for constructing the curved surface assembling component, the utilization rate requirements of different work types are different, so the total personnel utilization rate is calculated by adopting the work type weight:
Figure BDA0002071620240000084
wherein: k is a radical ofiThe weight value of the ith work type; h is the total number of work seeds; u. ofiThe personnel utilization rate of the ith work type; a larger calculated c-value indicates less free time for personnel and a higher personnel availability.
Step 103: determining a solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model; the method specifically comprises the following steps:
building a continuity model and the site scheduling algorithm model according to the assembly component, and determining a solving strategy of the site scheduling algorithm model;
and determining a solving strategy of the personnel scheduling algorithm model according to the personnel utilization rate model and the personnel scheduling algorithm model.
Solving the site scheduling algorithm model can transform the problem into a given backpack with weight L (site length) and n backpacks with weight Li(length of the assembled Member) and the current value is liLong term value of tiAn object of 1<i<n, requiring that objects be loaded into the backpack to maximize both the current value and the long-term value of the objects in the backpack. Let x beiIndicating that an object i is loaded into the backpack, xi0, 1. When x isiWhen 0, it means that the object is not loaded in the backpack; when x isiWhen 1, it means that the object is loaded in a backpack. Selecting which of the assembly membersThe problem of entering the current small field can be summarized as satisfying the corresponding constraint equation and maximizing the objective function by solving the vector X ═ X (X)1，x2，....xn) The problem of single-day layout of the whole field can be understood as the set of single-day layout of each small field. The set of building elements faced by the post-arranged site are the remaining building elements of the already arranged site. Therefore, the arrangement sequence of the sites determines the quality of the whole solution, and the sequence of the sites to be arranged is realized by dynamic planning.
The personnel scheduling algorithm model is divided into three parts: firstly, reading the processing sequence and position of an assembly component in a field, initializing the assembly component process, and distributing processing personnel according to the manufacturing sequence of the existing assembly component in the field; secondly, aiming at the flexible flow production characteristics of the curved assembly members, aiming at a virtual flow production mode that a processing person flows between the assembly members without clearance, aiming at the production sequence of the existing assembly members and the virtual flow production, initially distributing the processing person to each assembly member by calculation, and enabling the assembly members to start to produce after the distribution of the processing person is finished; and thirdly, in the production process of the subsequent assembly component, through the analysis of the production status of the assembly component, a machining staff redistribution technology is provided, the surplus of staff who already start to work the assembly component is avoided, and meanwhile, the staff on the assembly component is balanced, so that the aim of virtual continuous production of the assembly component production is maintained.
Aiming at the problem of personnel redistribution, on the basis of the initial personnel distribution of the assembly component, the required starting time of the assembly component to be produced is analyzed through the starting time and delivery date of the assembly component, meanwhile, the working time of a processing personnel on the initial assembly component in the time period is analyzed, an individual with a fault in the front and back processes in the initial assembly component is searched, the number of the processing personnel in the front process of the assembly component is calculated, the processing personnel is reduced, the processing personnel is connected with the starting time of the back process, the reduced processing personnel are arranged to be newly added to the assembly component for production, the continuous production of the adjacent processes of the initial assembly component is guaranteed, the processing personnel are provided for the production of the subsequent assembly component, and the processing personnel are generally formed to continuously circulate among the processes of the assembly components for production.
And (4) a personnel scheduling algorithm model. Aiming at the construction of a continuity objective function model of the assembly component, a continuity operation resource distribution objective among the processes is provided, the distribution objective is an objective function of a personnel scheduling algorithm model, the objective can be used as the basis of the initial production of the process of the assembly component, and the objective is essentially to ensure that the starting time and the ending time of the front process and the ending time of the rear process on the process route in the assembly component are mutually connected through the distribution mode of processing personnel. The calculation formula for this target is as follows:
Figure BDA0002071620240000101
wherein: w is ahThe weight of the continuity of the operation among the working procedures can be set according to the actual production needs; t isi，jA step j of task i; c. CjFor the production efficiency of the working process, njThe number of processing personnel corresponding to the procedure; h is1 maxminZ refers to the value that minimizes Z for the maximum value of the difference that all adjacent process operations meet. The assignment of the production personnel is done here, assuming that the production sequence and the position of the assembled components in the field have been determined. Since each assembled member has the largest producible number of persons, i.e. the number of working persons per process in the assembled member has an upper limit, and the number of persons working in one process of the assembled member cannot exceed this number. In the initial staff allocation process, because the assembly member production staff is sufficient, the upper limit number of staff is allocated to each assembly member process until all staff are allocated. The distribution mode can ensure that processing personnel can be fully utilized in the initial production state, thereby providing a foundation for subsequent continuous production.
In order to redistribute the processing personnel, on the basis of the distribution of the initial personnel of the assembly components, the required starting time of the assembly components needing to be produced subsequently is analyzed through the starting time and delivery date of the assembly components, the working time of the processing personnel on the initial assembly components in the time period is analyzed, individuals with faults occurring in the front and back procedures in the initial assembly components are searched, the number of the processing personnel in the front procedure of the assembly components is calculated and reduced, the processing personnel is connected with the starting time of the back procedure, the reduced processing personnel are arranged to be newly added to the assembly components for production, and therefore the continuous production of the adjacent procedures of the initial assembly components is guaranteed, the processing personnel are provided for the production of the subsequent assembly components, and the virtual flow production that the processing personnel continuously flow among the procedures of the assembly components is formed on the whole. The calculation formula of the number of the processing personnel can be extracted and adjusted as follows:
Figure BDA0002071620240000111
wherein: si，jStarting time of process j for assembling component i, Ei，jThe end time of step j for assembling the member i; c. CjFor the production efficiency of the working process, njThe number of workers for this process. The number of the processing personnel is continuously adjusted by calculating the time difference between the front process and the rear process of the assembled component, and the maximum number of the extractable processing personnel is calculated on the premise of ensuring that the ending time of the front process is as close as possible to the starting time of the subsequent process. All the extractable processing persons are taken as a set of processing persons of the subsequent new insertion assembly member process, and are distributed to the assembly member process to calculate the starting time and the ending time.
And (4) a site scheduling algorithm model. The assembled components are typically described in a one-dimensional manner when processed at a shop location, which is typically a long, narrow, and collapsible strip. Suppose there are m sites and n sets of components. Working j with a working time of PjThe submission date is rj. Work j must be composed of a member belonging to MjThe processing of the integrated field, and the total length of each independent field processing assembly component is limited. For the placement of the assembly components of each independent small field, modeling the assembly components as a knapsack problem, and selecting the assembly components needing to be arranged by using a dynamic programming algorithm; calculating the arrangement position of the selected assembly member by a clustering ruleThus obtaining the product.
A backpack ground with a given field length L and n assembling components with the length of Li(current value is liLong term value of tiAn object of 1<i<n) that requires that the object be loaded into the backpack to maximize both the current value and the long-term value of the object in the backpack. Let x beiIndicating that an object i is loaded into the backpack, xi0, 1. When x isiWhen 0, it means that the object is not loaded in the backpack; when x isiWhen 1, it means that the object is loaded in a backpack. Depending on the requirements of the problem, there are the following constraint equations and objective functions:
Figure BDA0002071620240000112
Figure BDA0002071620240000113
Figure BDA0002071620240000114
the problem of selecting which assemblage members enter the current plot can be summarized as satisfying the constraint equation and maximizing the solution vector X (X) of the objective function1，x2，…xn)。
Long-term value: the long-distance value of an assemblage member is related to its priority, and the higher the priority of an assemblage member is arranged, the higher the long-distance value obtained. The assembled components are prioritized and sorted by delivery date, the results of the sorting being, for example, No.1, No.4, No.4, NO6, … … No. m. Then the long-distance value of each assemblage member is M minus its rank. The problem of single-day arrangements of the entire site can then be understood as a collection of single-day arrangements of each small site. The set of building elements faced by the post-arranged site are the remaining building elements of the already arranged site. Therefore, the arrangement order of the sites determines the quality of the whole solution. For multi-day arrangement of the site, the process of single-day arrangement is repeated at each time node.
Dynamic rules: at a certain time point, a solution vector X, which satisfies the constraint equation shown in equation (10) and maximizes the objective functions shown in equations (11) and (12), is obtained as (X)1，x2，....xn). The length of the backpack is L. Order httpi 1(j) And httpi 2(j) Respectively, the maximum value of the objects that can be loaded into a backpack having a weight j, j ∈ (0, L), among the first i objects. Obviously, in the first i objects, some objects can be loaded into the backpack and some objects cannot be loaded into the backpack, so the following dynamic programming function can be obtained:
Figure BDA0002071620240000121
Figure BDA0002071620240000122
Figure BDA0002071620240000123
Figure BDA0002071620240000124
equations (13) and (14) show that loading the first i objects into a backpack with a load of 0, or loading 0 objects into a backpack with a load of j, yields a current value and a long-term value of 0. The primary formula of formula (15) indicates that: if the weight of the ith object is greater than the payload of the backpack, then the maximum value obtained for loading the i preceding objects is equal to the maximum value for loading the i-1 preceding objects. Http in formula 2i 2(j-li)+tiShows that: when the weight of the ith object is less than the load capacity of the backpack, if the ith object is loaded into the backpack with the load capacity of j, the value of the objects in the backpack is equal to the value of loading the front i-1 objects into the backpack with the load capacity of j-liValue obtained by the backpackCurrent value l of the ith objecti. If the ith object is not loaded in the backpack, the current value in the backpack is equal to the current value obtained by loading the first i-1 objects in the backpack with a payload of j. The current value obtained by the backpack is not always the same for both loading methods, so the largest one of the two is taken as the optimal current value obtained by loading the front i objects into the backpack with the load of j. The meaning of equation (16) is similar to equation (15) except that the current value is traded for long-term value.
To determine the particular object to be carried in the backpack, i.e. the particular assemblage to be arranged in the field, from httpn 1(L) and httpn 2The value of (L) is pushed backwards. Take the current value part as an example, if httpn 1(L) greater than httpn-1 1(L) indicating that the nth object is loaded in the backpack, and the first n-1 objects are loaded with a loading capacity of L-LiIn the backpack of (1); if httpn 1(L) is less than or equal to httpn-1 1(L) indicating that the nth object is not loaded in the backpack, and the first n-1 objects are loaded in the backpack with the load capacity of L; and so on until it is determined whether the first object is loaded into the backpack. This gives the relation:
if it is
Figure BDA0002071620240000131
Then
Figure BDA0002071620240000132
If it is
Figure BDA0002071620240000133
Then
Figure BDA0002071620240000134
If it is
Figure BDA0002071620240000135
Then
Figure BDA0002071620240000136
If it is
Figure BDA0002071620240000137
Then
Figure BDA0002071620240000138
The specific strategy implementation mode of the personnel scheduling method is as follows:
step 1: the existing positions and the processing sequence of the assembly components inside the field are read, the information of processing personnel of different work types in the field is initialized, and a basis is provided for subsequent dispatching personnel distribution.
Step 2: and aiming at a virtual flow continuous production mechanism, allocating the processing personnel according to an initialization personnel allocation technology, and allocating the upper limit number of the processing personnel for production and manufacturing each assembly component according to an initial sequence.
Step 3: and after all the personnel are completely distributed, calculating the processing time of each process of the assembly component for the initial assembly component according to the efficiency of the maximum processing personnel to obtain the ending time and the starting time of the previous process and the next process, and providing a basis for calculating the extractable processing personnel. The whole process is as follows:
process (1): detecting the assembly components with gaps in the machining time of the front and rear working procedures in the assembly component set, adding the assembly components into the assembly component set which can be allocated, and waiting for the subsequent permission.
Process (2): selecting one assembly component from the adjustable assembly component set, detecting the assembly component process with a gap, and extracting the end time of the previous process and the start time of the subsequent process of the previous process;
process (3): and according to the starting time and the ending time of the assembly component, continuously and circularly calculating the maximum number of processing personnel which can be extracted in the previous process of the assembly component, putting the processing personnel into a processing personnel set, and simultaneously recording the working time of the personnel. Returning to the process (2) to continue extracting the processing personnel, and entering a process (4) if all the assembly components in the assembly component set capable of being assembled are searched;
process (4): and calculating the planned starting time and the planned ending time of the subsequent assembly component in the field according to the production time of the personnel in the combination of the processing personnel, returning to the first step when the set of the processing personnel is empty, and continuously searching the existing time gap from the existing assembly component so as to extract the processing personnel for the production of the subsequent assembly component.
Step 4: and finally completing the scheduling distribution of the assembly member production and processing personnel through continuous circulation, and finally generating the assembly member virtual flow personnel scheduling scheme.
The site scheduling method comprises the following specific strategy implementation modes:
the single-day arrangement problem of a single independent field is solved by the following steps.
Step 1: initializing, i and j satisfying i is more than or equal to 0 and less than or equal to n and j is more than or equal to 1 and less than or equal to L, and enabling httpi 1(0)＝0，optp0 1(j)＝0，optpi 2(0)＝0，optp0 2(j)＝0。
Step 2: let i equal 1.
Step 3: calculation of the http according to equations (15) and (16)i 1(L)，optpi 2(L)。
Step 4: if i is equal to i +1, if i is greater than n, turning to the step (5); otherwise, go to step (3).
Step 5: let i equal n and j equal L.
Step 6: the vectors X are obtained according to the equations (13) to (16), respectively1＝(x1 1，x2 1，x3 1……xn 1)，X2＝(x1 2，x2 2，x3 2……xn 2)
Step 7: if X1＝X2Then X1Or X2Otherwise, step8 is performed.
Step 8: calculating solution X1Long distance value of (T)1And X2Long distance value of (T)2If T is2-T1Lambda is less than or equal to lambda (lambda is a parameter), then X1Is the final solution; otherwise, X2Is the final solution.
Step 104: and determining the small-schedule operation plan in the assembly component according to the solving strategy.
For specific analysis, the production plan may be analyzed as test data. Firstly, extracting assembly component tasks in a planning period from a database, wherein the tasks comprise the earliest start time, the latest completion time, uncertain upper and lower limit statistics and the like of corresponding assembly components and production requirements. And reading the size information and the construction period of the assembly member from the database according to the ship number and the assembly member number corresponding to the task. And then, calling the information of the field and the labor team in the database, and optimizing the building space and the labor scheduling of the assembly members in the ship assembly member building and scheduling system.
FIG. 3 is a diagram of a system for balancing and making a small schedule work plan in the assembled components of the present invention. As shown in fig. 3, a system for balancing and making a small-schedule work plan in an assembly member includes:
an obtaining module 301, configured to obtain uncertainty factors in a small-schedule work plan in an assembly component;
an uncertainty algorithm model determining module 302, configured to establish an uncertainty algorithm model of a small-schedule operation plan in an assembly component according to the uncertainty factors;
a solving strategy determining module 303, configured to determine a solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model;
and a medium-small schedule operation plan determining module 304, configured to determine a medium-small schedule operation plan in the assembly component according to the solution strategy.
The uncertainty factors include machining time and delivery time uncertainty.
The uncertainty algorithm model determining module 302 specifically includes:
the target function mathematical model determining unit is used for establishing a target function mathematical model of the small schedule operation plan in the assembly component according to the uncertainty factor;
the site scheduling algorithm model determining unit is used for establishing a site scheduling algorithm model of a small schedule operation plan in the assembled component according to the uncertainty factors;
and the personnel scheduling algorithm model determining unit is used for establishing a personnel scheduling algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors.
The objective function mathematical model determining unit specifically includes:
the assembly component construction continuity model determining subunit is used for establishing an assembly component construction continuity model of a small schedule operation plan in the assembly component according to the uncertainty factors;
wherein: f is a working daily system processing function and is set according to the actual production situation of the ship; si, j +1 is the starting time of the j +1 st procedure of the assembly member i; fi, j is the end time of the j-th process of the assembly component i; fi, max is the last completion time of the assembled component i; simin is the start-up time of the assembly component i;
the personnel utilization rate model determining subunit is used for establishing a personnel utilization rate model of the small schedule operation plan in the assembly component according to the uncertainty factors;
wherein: m is the total number of workers; cij is the actual labor time of the ith personnel on the jth day in hours; h is the working time length of the working day; w is the number of working days of a month.
The solving strategy determining module 303 specifically includes:
the solving strategy determining unit of the site scheduling algorithm model is used for building a continuity model and the site scheduling algorithm model according to the assembly component and determining the solving strategy of the site scheduling algorithm model;
and the solving strategy determining unit of the personnel scheduling algorithm model is used for determining the solving strategy of the personnel scheduling algorithm model according to the personnel utilization rate model and the personnel scheduling algorithm model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. A small and medium schedule operation plan balance making method in an assembly component is characterized by comprising the following steps:
acquiring uncertainty factors in small and medium schedule operation plans of the assembly component;
establishing an uncertainty algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors;
determining a solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model;
determining a small-schedule operation plan in the assembled component according to the solving strategy;
the uncertainty factors include processing time and delivery time uncertainty;
the establishing of the uncertainty algorithm model of the small-schedule operation plan in the assembly component according to the uncertainty factors specifically comprises the following steps:
establishing a target function mathematical model, a site scheduling algorithm model and a personnel scheduling algorithm model of the small schedule operation plan in the assembled component according to the uncertainty factor;
the establishing of the objective function mathematical model of the small-schedule operation plan in the assembly component according to the uncertainty factors specifically comprises the following steps:
building continuous model of assembly component for building small schedule operation plan in assembly component according to uncertainty factor
Figure FDA0003116323110000011
And personnel utilization rate model
Figure FDA0003116323110000012
Wherein: f is a working daily system processing function and is set according to the actual production situation of the ship; si，j+1The starting time of the j +1 st procedure of the assembly component i; fi，jThe end time of the j process of the assembly component i; fi maxThe final completion time for the assembled component i; si minThe start-up time of the assembly component i; n is the total number of the assembling components; m is the total number of the working procedures; r is the idle time ratio;
wherein: k is the total number of people of the same work type;
Figure FDA0003116323110000013
is the ith1Person j1Actual work hours of the day in hours; h is the working time length of the working day; w is the number of working days of a month; u is personnel utilization rate;
the determining a solution strategy of the uncertainty algorithm model according to the uncertainty algorithm model specifically includes:
building a continuity model and the site scheduling algorithm model according to the assembly component, and determining a solving strategy of the site scheduling algorithm model;
determining a solving strategy of a personnel scheduling algorithm model according to the personnel utilization rate model and the personnel scheduling algorithm model;
the personnel scheduling algorithm model is divided into three parts: firstly, reading the processing sequence and position of an assembly component in a field, initializing the assembly component process, and distributing processing personnel according to the manufacturing sequence of the existing assembly component in the field; secondly, aiming at the flexible flow production characteristics of the curved assembly members, aiming at a virtual flow production mode that a processing person flows between the assembly members without clearance, aiming at the production sequence of the existing assembly members and the virtual flow production, initially distributing the processing person to each assembly member by calculation, and enabling the assembly members to start to produce after the distribution of the processing person is finished; secondly, through the analysis of the production status of the assembly component in the production process of the subsequent assembly component, a machining personnel redistribution technology is provided, the surplus of personnel who start to work the assembly component is avoided, and meanwhile, the personnel on the assembly component are balanced, so that the aim of virtual continuous production of the assembly component production is maintained;
the personnel scheduling algorithm model is used for constructing a continuity objective function model aiming at the assembly component, and provides a continuity operation resource allocation target among the processes, wherein the allocation target is an objective function of the personnel scheduling algorithm model and can be used as a basis for initial production of the processes of the assembly component, and the target is essentially to ensure that the starting time and the ending time of the front and the rear processes on the process route in the assembly component are mutually connected in a distribution mode of processing personnel; the calculation formula for this target is as follows:
Figure FDA0003116323110000021
wherein: w is ahThe weight of the continuity of the operation among the working procedures can be set according to the actual production needs;
Figure FDA0003116323110000022
for task i2Step j of (2); n is2Is the total number of tasks; c. CjFor the production efficiency of the working process, njThe number of processing personnel corresponding to the procedure; h is1 maxFor all adjacent processes, processing is consistent with the maximum value of the difference, and minZ refers to the value of the minimum Z; assuming that the production sequence and the position of the assembled components in the field have been determined, the assignment of the production personnel is done here; because each assembly component has the largest producible number of people, namely the number of processing personnel in each process in the assembly component has an upper limit, and the number of people working in one process of the assembly component cannot exceed the number; in the initial personnel allocation process, because the production personnel of the assembly members are sufficient, each assembly member is processedDistributing the upper limit number of workers until all the workers are distributed; the distribution mode can ensure that processing personnel can be fully utilized in the initial production state, thereby providing a foundation for subsequent continuous production;
for the purpose of reassigning the processing personnel, the required starting time of the subsequently produced assembly component is analyzed by the processing time and delivery date thereof on the basis of the initial personnel assignment of the assembly component, meanwhile, the working time of the processing personnel on the initial assembly component in the time period is analyzed, the individuals with faults in the front and back procedures in the initial assembly component are searched, the number of the processing personnel in the front procedure of the assembly component is calculated to be reduced, the processing personnel is linked with the starting time of the back procedure, the reduced processing personnel are arranged to be newly added into the assembly component for production, therefore, the continuous production of the adjacent procedures of the initial assembly component is ensured, the processing personnel is provided for the production of the subsequent assembly component, the virtual flow production that the processing personnel uninterruptedly flow among the procedures of each assembly component is formed on the whole, and the quantity calculation formula of the processing personnel can be adjusted as follows:
Figure FDA0003116323110000031
wherein: si，jThe starting time of the process j for assembling the component i; the number of the processing personnel is continuously adjusted by calculating the time difference between the front process and the rear process of the assembled component, and the maximum number of the extractable processing personnel is calculated on the premise of ensuring that the ending time of the front process is as close as possible to the starting time of the subsequent process; taking all the extractable processing personnel as a set of processing personnel of a subsequent new insertion assembly member process, and distributing the set of processing personnel to the assembly member process to calculate the starting time and the ending time;
the specific strategy implementation mode of the personnel scheduling method is as follows:
step 1: reading the existing positions and the processing sequence of the assembly components in the field, initializing the information of processing personnel of different work types in the field, and providing a basis for subsequent dispatching personnel distribution;
step 2: aiming at a virtual flow continuous production mechanism, allocating processing personnel according to an initialization personnel allocation technology, and allocating the upper limit number of the processing personnel for production and manufacturing of each assembly component according to an initial sequence;
step 3: after all the personnel are completely distributed, calculating the processing time of each procedure of the assembly component for the initial assembly component according to the efficiency of the maximum processing personnel to obtain the ending time and the starting time of the previous procedure and the next procedure, and providing a basis for calculating the extractable processing personnel; the whole process is as follows:
process (1): detecting assembly components with gaps in the machining time of the front and rear working procedures in the assembly component set, adding the assembly components into the assembly component set which can be allocated, and waiting for subsequent permission;
process (2): selecting one assembly component from the adjustable assembly component set, detecting the assembly component process with a gap, and extracting the end time of the previous process and the start time of the subsequent process of the previous process;
process (3): continuously and circularly calculating the maximum number of processing personnel which can be extracted in the previous procedure of the assembly component according to the starting time and the ending time of the assembly component, putting the processing personnel into a processing personnel set, and simultaneously recording the working time of the personnel; returning to the process (2) to continue extracting the processing personnel, and entering a process (4) if all the assembly components in the assembly component set capable of being assembled are searched;
process (4): calculating the planned starting time and the planned ending time of the subsequent assembly component in the field according to the production time of the personnel in the combination of the processing personnel, returning to the first step when the set of the processing personnel is empty, and continuously searching the occurring time gap from the existing assembly component so as to extract the processing personnel for the production of the subsequent assembly component;
step 4: finally completing the scheduling distribution of the assembly member production and processing personnel through continuous circulation, and finally generating an assembly member virtual flow personnel scheduling scheme;
a site scheduling algorithm model is characterized in that when an assembly component is processed on a workshop station, the assembly component is generally described in a one-dimensional mode, and the workshop station is generally long and narrowBand collapse; suppose is provided with m1The situation of each site and n assembling components; work j2Processing time is PjThe submission date is rjWork j2Must be composed ofjThe method is characterized by comprising the following steps of (1) field processing is integrated, and the total length of each independent field processing assembly component is limited; for the placement of the assembly components of each independent small field, modeling the assembly components as a knapsack problem, and selecting the assembly components needing to be arranged by using a dynamic programming algorithm; the arrangement position of the selected assembly component is obtained through the calculation of a clustering rule;
a backpack ground with a given field length L and n assembling components with the length of LpThe current value is lpLong term value of tpAn object of 1<p<n, requiring to load the object into the backpack to maximize the current value and the long-term value of the object in the backpack; let x bepIndicating that the object p is carried in a backpack, xp0, 1; when x ispWhen 0, it means that the object is not loaded in the backpack; when x ispWhen 1, the object is loaded in the backpack; depending on the requirements of the problem, there are the following constraint equations and objective functions:
Figure FDA0003116323110000051
Figure FDA0003116323110000052
Figure FDA0003116323110000053
the problem of selecting which assemblage members enter the current plot can be summarized as satisfying the constraint equation and maximizing the solution vector X (X) of the objective function1，x2，…xn)；
Long-term value: the long-distance value of the assembly member is related to the priority of the assembly member, the higher the priority of the assembly member is arranged to be, the higher the long-distance value is obtained, the assembly member is set with the priority according to the delivery date and is sorted, and the sorting result is, for example, NO.1, NO.1, NO.1, NO4, NO.4, NO. 6, … … NO. M; then the long-distance value of each assemblage member is M minus its rank; then, the problem of single-day arrangement of the whole site can be understood as a set of single-day arrangement of each small site; the assembly component set faced by the post-arranged field is the rest assembly components of the arranged field; therefore, the arrangement sequence of the field determines the quality of the whole solution; for multi-day arrangement of the site, repeating the process of single-day arrangement at each time node;
dynamic rules: at a certain time point, a solution vector X satisfying the constraint equation shown in equation (3) and maximizing the objective functions shown in equations (4) and (5) is obtained as (X)1，x2，....xn) The length of the backpack is L; order httpp 1(q) and httpp 2(q) represents the maximum value of objects that can be loaded into a backpack of weight q, among the first p objects, respectively, q ∈ (0, L); obviously, in the first p objects at this time, some objects can be loaded into the backpack, and some objects cannot be loaded into the backpack, so the following dynamic programming functions can be obtained:
Figure FDA0003116323110000054
Figure FDA0003116323110000055
Figure FDA0003116323110000056
Figure FDA0003116323110000057
equations (6) and (7) show that the first p objects are loadedThe weight is 0 backpack, or 0 objects are loaded into the backpack with the load weight of q, and the obtained current value and the long-term value are both 0; the primary formula of formula (8) indicates that: if the weight of the p-th object is larger than the load capacity of the backpack, the maximum value obtained by loading the front p objects is equal to the maximum value of loading the front p-1 objects; in the second formula
Figure FDA0003116323110000061
Shows that: when the weight of the p-th object is less than the load weight of the backpack, if the p-th object is loaded into a backpack having a load weight of q, the value of the objects in the backpack is equal to the value of the p-1 objects in front loaded into a backpack having a load weight of q-lpThe value obtained for the backpack plus the current value l for the pth objectp(ii) a If the pth object is not loaded in the backpack, the current value in the backpack is equal to the current value obtained by loading the previous p-1 objects in the backpack with a payload of q; in the two loading methods, the current values obtained in the backpack are not necessarily the same, so the largest one of the two is taken as the optimal current value obtained by loading the front p objects into the backpack with the load capacity of q, and the meaning of the formula (9) is similar to that of the formula (8) except that the current value is changed into the long-term value;
to determine the particular object to be carried in the backpack, i.e. the particular assemblage to be arranged in the field, from httpn 1(L) and httpn 2The value of (L) is pushed backwards; take the current value part as an example, if httpn 1(L) greater than httpn-1 1(L) indicating that the nth assembling member is loaded in the backpack, the front n-1 assembling member is loaded with a load of L-LpIn the backpack of (1); if httpn 1(L) is less than or equal to httpn-1 1(L) indicating that the nth assemblage member is not loaded in the backpack, the front n-1 assemblage member is loaded in the backpack with the load capacity of L; and so on until it is determined whether the first object is loaded into the backpack; this gives the relation:
if it is
Figure FDA0003116323110000062
Then
Figure FDA0003116323110000063
If it is
Figure FDA0003116323110000064
Then
Figure FDA0003116323110000065
If it is
Figure FDA0003116323110000066
Then
Figure FDA0003116323110000067
If it is
Figure FDA0003116323110000068
Then
Figure FDA0003116323110000069
The site scheduling method comprises the following specific strategy implementation modes:
the single-day arrangement problem of a single independent field is solved according to the following steps:
step 1: initializing, for p and q satisfying p is more than or equal to 0 and less than or equal to n and q is more than or equal to 1 and less than or equal to L, making httpp 1(0)＝0，optp0 1(q)＝0，optpp 2(0)＝0，optp0 2(q)＝0；
Step 2: let p be 1;
step 3: calculation of the http according to equations (8) and (9)p 1(L)，optpp 2(L)；
Step 4: if p is p +1, if p is more than n, turning to the step (5); otherwise, turning to the step (3);
step 5: let p be n, q be L;
step 6: the vectors X are obtained according to the equations (6) to (9)1＝(x1 1，x2 1，x3 1……xn 1)，X2＝(x1 2，x2 2，x3 2……xn 2)；
Step 7: if X1＝X2Then X1Or X2If not, executing step 8;
step 8: calculating solution X1Long distance value of (T)1And X2Long distance value of (T)2If T is2-T1Lambda is less than or equal to lambda (lambda is a parameter), then X1Is the final solution; otherwise, X2Is the final solution.
2. A small and medium schedule work plan balance making system in an assembly component is characterized by comprising:
the acquisition module is used for acquiring uncertainty factors in small and medium schedule operation plans of the assembly component;
the uncertainty algorithm model determining module is used for establishing an uncertainty algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factors;
the solving strategy determining module is used for determining the solving strategy of the uncertainty algorithm model according to the uncertainty algorithm model;
the medium and small schedule operation plan determining module is used for determining the medium and small schedule operation plan of the assembly component according to the solving strategy;
the uncertainty factors include processing time and delivery time uncertainty;
the uncertainty algorithm model determination module specifically comprises:
the target function mathematical model determining unit is used for establishing a target function mathematical model of the small schedule operation plan in the assembly component according to the uncertainty factor;
the site scheduling algorithm model determining unit is used for establishing a site scheduling algorithm model of a small schedule operation plan in the assembled component according to the uncertainty factors;
the personnel scheduling algorithm model determining unit is used for establishing a personnel scheduling algorithm model of the small schedule operation plan in the assembly component according to the uncertainty factor;
the objective function mathematical model determining unit specifically includes:
the assembly component construction continuity model determining subunit is used for establishing an assembly component construction continuity model of a small schedule operation plan in the assembly component according to the uncertainty factors
Figure FDA0003116323110000081
Wherein: f is a working daily system processing function and is set according to the actual production situation of the ship; si，j+1The starting time of the j +1 st procedure of the assembly component i; fi，jThe end time of the j process of the assembly component i; fi maxThe final completion time for the assembled component i; si minThe start-up time of the assembly component i; (ii) a n is the total number of the assembling components; m is the total number of the working procedures; r is the idle time ratio;
a personnel utilization rate model determining subunit, configured to establish a personnel utilization rate model of the small-schedule operation plan in the assembled component according to the uncertainty factor
Figure FDA0003116323110000082
Wherein: k is the total number of people of the same work type;
Figure FDA0003116323110000083
is the ith1Person j1Actual work hours of the day in hours; h is the working time length of the working day; w is the number of working days of a month; u is personnel utilization rate;
the determining a solution strategy of the uncertainty algorithm model according to the uncertainty algorithm model specifically includes:
building a continuity model and the site scheduling algorithm model according to the assembly component, and determining a solving strategy of the site scheduling algorithm model;
determining a solving strategy of a personnel scheduling algorithm model according to the personnel utilization rate model and the personnel scheduling algorithm model;
the personnel scheduling algorithm model is divided into three parts: firstly, reading the processing sequence and position of an assembly component in a field, initializing the assembly component process, and distributing processing personnel according to the manufacturing sequence of the existing assembly component in the field; secondly, aiming at the flexible flow production characteristics of the curved assembly members, aiming at a virtual flow production mode that a processing person flows between the assembly members without clearance, aiming at the production sequence of the existing assembly members and the virtual flow production, initially distributing the processing person to each assembly member by calculation, and enabling the assembly members to start to produce after the distribution of the processing person is finished; secondly, through the analysis of the production status of the assembly component in the production process of the subsequent assembly component, a machining personnel redistribution technology is provided, the surplus of personnel who start to work the assembly component is avoided, and meanwhile, the personnel on the assembly component are balanced, so that the aim of virtual continuous production of the assembly component production is maintained;
the personnel scheduling algorithm model is used for constructing a continuity objective function model aiming at the assembly component, and provides a continuity operation resource allocation target among the processes, wherein the allocation target is an objective function of the personnel scheduling algorithm model and can be used as a basis for initial production of the processes of the assembly component, and the target is essentially to ensure that the starting time and the ending time of the front and the rear processes on the process route in the assembly component are mutually connected in a distribution mode of processing personnel; the calculation formula for this target is as follows:
Figure FDA0003116323110000091
wherein: w is ahT can be set according to actual production requirements for inter-process operation continuity weighti2,jFor task i2Step j of (2); n is2Is the total number of tasks; c. CjFor the production efficiency of the working process, njCorresponding to this procedureThe number of processing personnel; h is1 maxFor all adjacent processes, processing is consistent with the maximum value of the difference, and minZ refers to the value of the minimum Z; assuming that the production sequence and the position of the assembled components in the field have been determined, the assignment of the production personnel is done here; because each assembly component has the largest producible number of people, namely the number of processing personnel in each process in the assembly component has an upper limit, and the number of people working in one process of the assembly component cannot exceed the number; in the initial personnel allocation process, because the assembly member production personnel are sufficient, the upper limit number of workers is allocated to the procedure of each assembly member until all the workers are allocated; the distribution mode can ensure that processing personnel can be fully utilized in the initial production state, thereby providing a foundation for subsequent continuous production;
for the purpose of reassigning the processing personnel, the required starting time of the subsequently produced assembly component is analyzed by the processing time and delivery date thereof on the basis of the initial personnel assignment of the assembly component, meanwhile, the working time of the processing personnel on the initial assembly component in the time period is analyzed, the individuals with faults in the front and back procedures in the initial assembly component are searched, the number of the processing personnel in the front procedure of the assembly component is calculated to be reduced, the processing personnel is linked with the starting time of the back procedure, the reduced processing personnel are arranged to be newly added into the assembly component for production, therefore, the continuous production of the adjacent procedures of the initial assembly component is ensured, the processing personnel is provided for the production of the subsequent assembly component, the virtual flow production that the processing personnel uninterruptedly flow among the procedures of each assembly component is formed on the whole, and the quantity calculation formula of the processing personnel can be adjusted as follows:
Figure FDA0003116323110000101
wherein: si，jThe starting time of the process j for assembling the component i; the number of the processing personnel is continuously adjusted by calculating the time difference between the front process and the rear process of the assembled component, and the end of the front process is ensuredCalculating the maximum number of extractable processing personnel on the premise that the time is as close as possible to the starting time of the subsequent process; taking all the extractable processing personnel as a set of processing personnel of a subsequent new insertion assembly member process, and distributing the set of processing personnel to the assembly member process to calculate the starting time and the ending time;
the specific strategy implementation mode of the personnel scheduling method is as follows:
step 1: reading the existing positions and the processing sequence of the assembly components in the field, initializing the information of processing personnel of different work types in the field, and providing a basis for subsequent dispatching personnel distribution;
step 2: aiming at a virtual flow continuous production mechanism, allocating processing personnel according to an initialization personnel allocation technology, and allocating the upper limit number of the processing personnel for production and manufacturing of each assembly component according to an initial sequence;
step 3: after all the personnel are completely distributed, calculating the processing time of each procedure of the assembly component for the initial assembly component according to the efficiency of the maximum processing personnel to obtain the ending time and the starting time of the previous procedure and the next procedure, and providing a basis for calculating the extractable processing personnel; the whole process is as follows:
process (1): detecting assembly components with gaps in the machining time of the front and rear working procedures in the assembly component set, adding the assembly components into the assembly component set which can be allocated, and waiting for subsequent permission;
process (2): selecting one assembly component from the adjustable assembly component set, detecting the assembly component process with a gap, and extracting the end time of the previous process and the start time of the subsequent process of the previous process;
process (3): continuously and circularly calculating the maximum number of processing personnel which can be extracted in the previous procedure of the assembly component according to the starting time and the ending time of the assembly component, putting the processing personnel into a processing personnel set, and simultaneously recording the working time of the personnel; returning to the process (2) to continue extracting the processing personnel, and entering a process (4) if all the assembly components in the assembly component set capable of being assembled are searched;
process (4): calculating the planned starting time and the planned ending time of the subsequent assembly component in the field according to the production time of the personnel in the combination of the processing personnel, returning to the first step when the set of the processing personnel is empty, and continuously searching the occurring time gap from the existing assembly component so as to extract the processing personnel for the production of the subsequent assembly component;
step 4: finally completing the scheduling distribution of the assembly member production and processing personnel through continuous circulation, and finally generating an assembly member virtual flow personnel scheduling scheme;
the method comprises the following steps that a site scheduling algorithm model is adopted, when an assembly component is processed on a workshop station, one-dimensional description is generally adopted, and the workshop station is a long and narrow collapse zone generally; suppose is provided with m1The situation of each site and n assembling components; work j2Processing time is PjThe submission date is rjWork j2Must be composed ofjThe method is characterized by comprising the following steps of (1) field processing is integrated, and the total length of each independent field processing assembly component is limited; for the placement of the assembly components of each independent small field, modeling the assembly components as a knapsack problem, and selecting the assembly components needing to be arranged by using a dynamic programming algorithm; the arrangement position of the selected assembly component is obtained through the calculation of a clustering rule;
a backpack ground with a given field length L and n assembling components with the length of LpThe current value is lpLong term value of tpAn object of 1<p<n, requiring to load the object into the backpack to maximize the current value and the long-term value of the object in the backpack; let x bepIndicating that the object p is carried in a backpack, xp0, 1; when x ispWhen 0, it means that the object is not loaded in the backpack; when x ispWhen 1, the object is loaded in the backpack; depending on the requirements of the problem, there are the following constraint equations and objective functions:
Figure FDA0003116323110000111
Figure FDA0003116323110000112
Figure FDA0003116323110000113
the problem of selecting which assemblage members enter the current plot can be summarized as satisfying the constraint equation and maximizing the solution vector X (X) of the objective function1，x2，…xn)；
Long-term value: the long-distance value of the assembly member is related to the priority of the assembly member, the higher the priority of the assembly member is arranged to be, the higher the long-distance value is obtained, the assembly member is set with the priority according to the delivery date and is sorted, and the sorting result is, for example, NO.1, NO.1, NO.1, NO4, NO.4, NO. 6, … … NO. M; then the long-distance value of each assemblage member is M minus its rank; then, the problem of single-day arrangement of the whole site can be understood as a set of single-day arrangement of each small site; the assembly component set faced by the post-arranged field is the rest assembly components of the arranged field; therefore, the arrangement sequence of the field determines the quality of the whole solution; for multi-day arrangement of the site, repeating the process of single-day arrangement at each time node;
dynamic rules: at a certain time point, a solution vector X satisfying the constraint equation shown in equation (3) and maximizing the objective functions shown in equations (4) and (5) is obtained as (X)1，x2，....xn) The length of the backpack is L; order httpp 1(q) and httpp 2(q) represents the maximum value of objects that can be loaded into a backpack of weight q, among the first p objects, respectively, q ∈ (0, L); obviously, in the first p objects at this time, some objects can be loaded into the backpack, and some objects cannot be loaded into the backpack, so the following dynamic programming functions can be obtained:
Figure FDA0003116323110000121
Figure FDA0003116323110000122
Figure FDA0003116323110000123
Figure FDA0003116323110000124
equations (6) and (7) show that loading p objects in front into a backpack with a load of 0, or loading 0 objects into a backpack with a load of q, results in a current value and a long-term value of 0; the primary formula of formula (8) indicates that: if the weight of the p-th object is larger than the load capacity of the backpack, the maximum value obtained by loading the front p objects is equal to the maximum value of loading the front p-1 objects; in the second formula
Figure FDA0003116323110000125
Shows that: when the weight of the p-th object is less than the load weight of the backpack, if the p-th object is loaded into a backpack having a load weight of q, the value of the objects in the backpack is equal to the value of the p-1 objects in front loaded into a backpack having a load weight of q-lpThe value obtained for the backpack plus the current value l for the pth objectp(ii) a If the pth object is not loaded in the backpack, the current value in the backpack is equal to the current value obtained by loading the previous p-1 objects in the backpack with a payload of q; in the two loading methods, the current values obtained in the backpack are not necessarily the same, so the largest one of the two is taken as the optimal current value obtained by loading the front p objects into the backpack with the load capacity of q, and the meaning of the formula (9) is similar to that of the formula (8) except that the current value is changed into the long-term value;
to determine the particular object to be carried in the backpack, i.e. the particular assemblage to be arranged in the field, from httpn 1(L) and httpn 2The value of (L) is pushed backwards; take the current value part as an example, if httpn 1(L) greater than httpn-1 1(L) is as shownWhen n assembling members are loaded in the backpack, the front n-1 assembling members are loaded with a load of L-LpIn the backpack of (1); if httpn 1(L) is less than or equal to httpn-1 1(L) indicating that the nth assemblage member is not loaded in the backpack, the front n-1 assemblage member is loaded in the backpack with the load capacity of L; and so on until it is determined whether the first object is loaded into the backpack; this gives the relation:
if it is
Figure FDA0003116323110000131
Then
Figure FDA0003116323110000132
If it is
Figure FDA0003116323110000133
Then
Figure FDA0003116323110000134
If it is
Figure FDA0003116323110000135
Then
Figure FDA0003116323110000136
If it is
Figure FDA0003116323110000137
Then
Figure FDA0003116323110000138
The site scheduling method comprises the following specific strategy implementation modes:
the single-day arrangement problem of a single independent field is solved according to the following steps:
step 1: initializing, for p and q satisfying p is more than or equal to 0 and less than or equal to n and q is more than or equal to 1 and less than or equal to L, making httpp 1(0)＝0，optp0 1(q)＝0，optpp 2(0)＝0，optp0 2(q)＝0；
Step 2: let p be 1;
step 3: calculation of the http according to equations (8) and (9)p 1(L)，optpp 2(L)；
Step 4: if p is p +1, if p is more than n, turning to the step (5); otherwise, turning to the step (3);
step 5: let p be n, q be L;
step 6: the vectors X are obtained according to the equations (6) to (9)1＝(x1 1，x2 1，x3 1……xn 1)，X2＝(x1 2，x2 2，x3 2……xn 2)；
Step 7: if X1＝X2Then X1Or X2If not, executing step 8;
step 8: calculating solution X1Long distance value of (T)1And X2Long distance value of (T)2If T is2-T1Lambda is less than or equal to lambda (lambda is a parameter), then X1Is the final solution; otherwise, X2Is the final solution.
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